from sklearn.model_selection import  validation_curve#将learning_curve改为validation_curve
from sklearn.datasets import load_digits
from sklearn.svm import SVC
import matplotlib.pyplot as plt
import numpy as np
#引入数据
digits=load_digits()
X=digits.data
y=digits.target
 
#改变param来观察Loss函数情况
param_range=np.logspace(-6,-2.3,5)
train_loss,test_loss=validation_curve(
    SVC(),X,y,param_name='gamma',param_range=param_range,cv=10,
    scoring='neg_mean_squared_error'
)
train_loss_mean=-np.mean(train_loss,axis=1)
test_loss_mean=-np.mean(test_loss,axis=1)
 
plt.figure()
plt.plot(param_range,train_loss_mean,'o-',color='r',label='Training')
plt.plot(param_range,test_loss_mean,'o-',color='g',label='Cross-validation')
plt.xlabel('gamma')
plt.ylabel('loss')
plt.legend(loc='best')
plt.show()